Click here to download the bibtex version of the bibliography.

Author |
Year |
Title |
Journal/Book |
Volume |
Number |
Pages |
---|---|---|---|---|---|---|

Bagnall, A. & Janacek, G. | 2014 | A run length transformation for discriminating between auto regressive time series | Journal of Classification | 31 | 154--178 | |

Bagnall, A.; Lines, J.; Hills, J. & Bostrom, A. | 2015 | Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles | IEEE Transactions on Knowledge and Data Engineering | 27 | 2522--2535 | |

Batista, G.; Keogh, E.; Tataw, O. & deSouza, V. | 2014 | CID: an efficient complexity-invariant distance measure for time series | Data Mining and Knowledge Discovery | 28 | 3 | 634--669 |

Baydogan, M. & Runger, G. | 2015 | Time series representation and similarity based on local autopatterns | Data Mining and Knowledge Discovery | |||

Baydogan, M.; Runger, G. & Tuv, E. | 2013 | A Bag-of-Features Framework to Classify Time Series | IEEE Transactions on Pattern Analysis and Machine Intelligence | 25 | 11 | 2796--2802 |

Bostrom, A. & Bagnall, A. | 2017 | Binary Shapelet Transform for Multiclass Time Series Classification | Transactions on Large-Scale Data and Knowledge Centered Systems | 32 | 24--46 | |

Corduas, M. & Piccolo, D. | 2008 | Time series clustering and classification by the autoregressive metric | Computational Statistics and Data Analysis | 52 | 4 | 1860--1872 |

Deng, H.; Runger, G.; Tuv, E. & Vladimir, M. | 2013 | A time series forest for classification and feature extraction | Information Sciences | 239 | 142--153 | |

Ding, H.; Trajcevski, G.; Scheuermann, P.; Wang, X. & Keogh, E. | 2008 | Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures | Proc. 34th VLDB | |||

Fulcher, B. & Jones, N. | 2014 | Highly comparative feature-based time-series classification | IEEE Transactions on Knowledge and Data Engineering | 26 | 12 | 3026--3037 |

Gorecki, T. & Luczak, M. | 2013 | Using derivatives in time series classification | Data Mining and Knowledge Discovery | 26 | 2 | 310--331 |

Gorecki, T. & Luczak, M. | 2014 | Non-isometric transforms in time series classification using DTW | Knowledge-Based Systems | 61 | 98--108 | |

Grabocka, J. & Schmidt-Thieme, L. | 2014 | Invariant time-series factorization | Data Mining and Knowledge Discovery | 28 | 5 | 1455--1479 |

Grabocka, J.; Schilling, N.; Wistuba, M. & Schmidt-Thieme, L. | 2014 | Learning Time-Series Shapelets | Proc. 20th SIGKDD | |||

Hills, J.; Lines, J.; Baranauskas, E.; Mapp, J. & Bagnall, A. | 2014 | Classification of time series by shapelet transformation | Data Mining and Knowledge Discovery | 28 | 4 | 851--881 |

Jeong, Y.; Jeong, M. & Omitaomu, O. | 2011 | Weighted dynamic time warping for time series classification | Pattern Recognition | 44 | 2231--2240 | |

Kate, R. | 2015 | Using dynamic time warping distances as features for improved time series classification | Data Mining and Knowledge Discovery | |||

Keogh, E. & Pazzani, M. | 2001 | Derivative dynamic time warping | Proc. 1st SDM | |||

Lin, J.; Keogh, E.; Li, W. & Lonardi, S. | 2007 | Experiencing SAX: A novel symbolic representation of time series | Data Mining and Knowledge Discovery | 15 | 2 | 107--144 |

Lin, J.; Khade, R. & Li, Y. | 2012 | Rotation-invariant similarity in time series using bag-of-patterns representation | Journal of Intelligent Information Systems | 39 | 2 | 287-315 |

Lines, J. & Bagnall, A. | 2015 | Time Series Classification with Ensembles of Elastic Distance Measures | Data Mining and Knowledge Discovery | 29 | 565--592 | |

Marteau, P. | 2009 | Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching | IEEE Transactions on Pattern Analysis and Machine Intelligence | 31 | 2 | 306--318 |

Mueen, A.; Keogh, E. & Young, N. | 2011 | Logical-Shapelets: An Expressive Primitive for Time Series Classification | Proc. 17th SIGKDD | |||

Rakthanmanon, T. & Keogh, E. | 2013 | Fast-Shapelets: A Fast Algorithm for Discovering Robust Time Series Shapelets | Proc. 13th SDM | |||

Ratanamahatana, C. & Keogh, E. | 2005 | Three Myths about Dynamic Time Warping Data Mining | Proc. 5th SDM | |||

Rath, T. & Manamatha, R. | 2003 | Word image matching using dynamic time warping | Proc. Computer Vision and Pattern Recognition | |||

Rodriguez, J.; Alonso, C. & Maestro, J. | 2005 | Support Vector Machines of Interval-based Features for Time Series Classification | Knowledge-Based Systems | 18 | 171--178 | |

Schafer, P. | 2015 | The BOSS is concerned with time series classification in the presence of noise | Data Mining and Knowledge Discovery | 29 | 6 | 1505--1530 |

Senin, P. & Malinchik, S. | 2013 | SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model | Proc. 13th IEEE ICDM | |||

Silva, D.; de Souza, V. & Batista, G. | 2013 | Time Series Classification Using Compression Distance of Recurrence Plots | Proc. 13th IEEE ICDM | |||

Stefan, A.; Athitsos, V. & Das, G. | 2013 | The Move-Split-Merge Metric for Time Series | IEEE Transactions on Knowledge and Data Engineering | 25 | 6 | 1425--1438 |

Wang, X.; Mueen, A.; Ding, H.; Trajcevski, G.; Scheuermann, P. & Keogh, E. | 2013 | Experimental comparison of representation methods and distance measures for time series data | Data Mining and Knowledge Discovery | 26 | 2 | 275--309 |

Ye, L. & Keogh, E. | 2011 | Time series shapelets: a novel technique that allows accurate, interpretable and fast classification | Data Mining and Knowledge Discovery | 22 | 2 | 149-182 |

Baydogan, M. & Runger G. | 2016 | Time series representation and similarity based on local autopatterns | Data Mining and Knowledge Discovery | 30 | 2 | 476--509 |

Karlsson, I.; Papapetrou, P. & Bostrom, H. | 2016 | Generalized random shapelet forests | Data Mining and Knowledge Discovery | 30 | 5 | 1053--1085 |

Yeh, M.; Zhu, Y.; Ulanova, L.; Begum, N.; Ding, Y.; Dau, H.; Silva, D.; Mueen, A. & Keogh, E. | 2018 | Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile | Data Mining and Knowledge Discovery | 32 | 1 | 83--123 |

Lines, J.; Taylor, S. & Bagnall, A. | 2018 | Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-based Ensembles | ACM Transactions Knowledge Discovery from Data | 12 | 5 | 1-36 |

Schafer, P. | 2016 | Scalable time series classification | Data Mining and Knowledge Discovery | 30 | 5 | 1273--1298 |

Schafer, P. & Leser, U. | 2017 | Fast and accurate time series classification with WEASEL | Proc. of 26th ACM CIKM | |||

Fulcher, B. & Jones, N. | 2017 | hctsa: A computational framework for automated time-series phenotyping using massive feature extraction | Cell Systems | 5 | 5 | 527--531 |

Karim, F.; Majumdar, S.; Darabi, H. & Chen, S. | 2017 | LSTM fully convolutional networks for time series classification | IEEE access | 6 | 1662--1669 | |

Bagnall, A.; Lines, J.; Bostrom, A.; Large, J. & Keogh, E. | 2017 | The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances | Data Mining and Knowledge Discovery | 31 | 3 | 606--660 |

Tan, C.; Herrman, M.; Forestier, G.; Webb, G. & Petitjean, F. | 2018 | Efficient search of the best warping window for Dynamic Time Warping | Proc. of 18th SDM | |||

Dau, H.; Silva, D.; Petitjean, F.; Forestier, G.; Bagnall, A. & Keogh, E. | 2018 | Optimizing Dynamic Time Warping's Window Width for Time Series Data Mining Applications | Data Mining and Knowledge Discovery | 32 | 4 | 1074--1120 |

Large, J.; Bagnall, A.; Malinowski, S. & Tavenard, R. | 2019 | On Time Series Classification with Dictionary-Based Classifiers | Intelligent Data Analysis | 23 | 5 | |

Lucas, B.; Shifaz, A.; Pelletier, C.; O'Neill, L.; Zaidi, N.; Goethals, B; Petitjean, F. & Webb, G. | 2019 | Proximity Forest: an effective and scalable distance-based classifier for time series | Data Mining and Knowledge Discovery | 33 | 3 | 607--635 |

Abanda, A.; Mori, U. & Lozano, J. | 2019 | A review on distance based time series classification | Data Mining and Knowledge Discovery | 33 | 2 | 378--412 |

Flynn, M.; Large, J. & Bagnall, A. | 2019 | The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy | Proc. of 14th HAIS | |||

Large, J.; Southam, P. & Bagnall, A. | 2019 | Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? | Proc. of 14th HAIS | |||

Guijo-Rubio, D.; Gutierrez, P.; Tavenard, R. & Bagnall, A. | 2019 | A Hybrid Approach to Time Series Classification with Shapelets | Proc. of 20th IDEAL | |||

Oastler, G. & Lines, J. | 2019 | A Significantly Faster Elastic-Ensemble for Time-Series Classification | Proc. of 20th IDEAL | |||

Middlehurst, M.; Vickers, W. & Bagnall, A. | 2019 | Scalable dictionary classifiers for time series classification | Proc. of 20th IDEAL | |||

Fawaz, H.; Forestier, G.; Weber, J.; Idoumghar, L. & Muller, P. | 2019 | Deep learning for time series classification: a review | Data Mining and Knowledge Discovery | 33 | 4 | 917--963 |

Lubba, C.; Sethi, S.; Knaute, P.; Schultz, S.; Fulcher, B. & Jones, N. | 2019 | catch22: canonical time-series characteristics | Data Mining and Knowledge Discovery | 33 | 6 | 1821--1852 |

Fawaz, H.; Lucas, B.; Forestier, G.; Pelletier, C.; Schmidt, D.; Weber, J.; Webb, G.; Idoumghar, L.; Muller, P. & Petitjean, F. | 2020 | InceptionTime: finding AlexNet for time series classification | Data Mining and Knowledge Discovery | 34 | 6 | 1936--1962 |

Le Nguyen, T.; Gsponer, S.; Ilie, I.; O'Reilly, M. & Ifrim, G. | 2019 | Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations | Data Mining and Knowledge Discovery | 33 | 4 | 1183--1222 |

Shifaz, A.; Pelletier, C.; Petitjean, F. & Webb, G. | 2020 | Ts-chief: A scalable and accurate forest algorithm for time series classification | Data Mining and Knowledge Discovery | 1--34 | ||

Dempster, A.; Petitjean, F. & Webb, G. | 2020 | ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels | Data Mining and Knowledge Discovery | 34 | 1454--1495 | |

Middlehurst, M.; Large, J. & Bagnall, A. | 2020 | The Canonical Interval Forest (CIF) Classifier for Time Series Classification | Proc. of 8th IEEE BigData | |||

Middlehurst, M.; Large, J.; Cawley, G. & Bagnall, A. | 2020 | The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification | Proc. of 20th ECML-PKDD | |||

Bagnall, A.; Flynn, M.; Large, J.; Lines, J. & Middlehurst, M. | 2020 | On the usage and performance of HIVE-COTE v1.0 | Proc. of 5th AALTD | |||

Cabello, N.; Naghizade, E.; Qi, J. & Kulik, L. | 2020 | Fast and Accurate Time Series Classification Through Supervised Interval Search | Proc. of 20th IEEE ICDM |